1. Field of the Invention
The present invention relates to data search systems designed for personnel recruitment using fuzzy criteria.
2. Description of the Related Art
Recruiters have two main problems when they are looking for candidates:
a. There are not enough candidates
b. It is hard to assess candidates before an interview:                i. Qualification of a candidate        ii. Probability of a particular candidate to be hired by a particular hiring manager for reasons not directly related to qualification but rather related to “culture fit”.        
To find enough candidates, recruiters use different resources:
A conventional method of searching for candidates is to search through so-called “job boards,” where “active” candidates (i.e., those, who are currently looking for job) upload their CVs (resumes) providing a detailed account of their professional skills, education, certifications, work experience at different companies and references. The problem of this conventional method is that the highly qualified candidates usually change their job “by invitation,” so that they have no need to write up a CV and post it on a job website. Thus, this conventional method can only be used to look for “active” candidates, which is not sufficient.
Many candidates have their profiles/accounts on various professional social networks and their own websites. The candidates can also join professional discussions on Internet forums and, in case with programmers, participate in open-source projects. In order to find job candidates, recruiters use various job-seeking services (e.g., dice.com, monster.com, superjob.ru, headhunter.ru, etc.), professional social networks (LinkedIn, Xing, Viadeo, Zerply, Moikrug.ru,), recreational social networks (Facebook, Google Plus, Myspace, VKontakte Twitter, Tumblr,), Q&A forums (rsdn.ru, stackoverflow.com, quora.com, expert-exchange.com), open-source software repositories (e.g., Github, Google Code, Bitbucket, SourceForge, Launchpad, Redmine).
Nonetheless, it is often difficult to find a suitable candidate, since they might have no LinkedIn profile at all, or their profile might provide only partial data (e.g., a Github profile may contain only the name of the candidate and a list of projects they are participating in, and a LinkedIn profile may provide only the name and the city of the candidate, but no skills description or projects they have participated in).
As discussed above, a known method for looking for “passive” candidates using professional social networks (e.g., LinkedIn, Xing, Viadeo, Zerply, Moikrug.ru, etc.) is inefficient. The main problem of this method is that the search yields only the data that the candidate has filled in their profile. Highly qualified “passive” candidates usually have no need to give a full account of their achievements, skills and work experience in their respective field. The reasons are the same as for the lack of their CVs on the job boards. For example, highly qualified software developers can be distinguished mainly by their participation in open-source software development projects or activity in giving answers to difficult problems posted on professional forums dedicated to software development (e.g., Stackoverflow).
Yet another known method is to search for information about potential job candidates (including “passive” ones) on different Internet resources. The problem of this known method is that each individual profile/account of the candidate might miss specific data needed to identify a qualified candidate, such as their professional skills, work experience, projects they have participated in, and/or contacts. Another problem is that the search might yield different profiles of the same candidate, but from different resources that may have been viewed earlier. Thus, it increases the effort needed staff recruiters, who have to look through various resources and spend much more time checking the same candidates, than if they were monitoring only one resource.
Accordingly, there is a need in the art for a system, which creates a single “distributed profile” for the candidate, which would combine all candidates' profiles/accounts on different network resources and supply the missing information for each individual profile.
Assessing candidates before an interview is hard to do, and leads to very broad “recruitment funnels”—companies have to interview a lot of candidates (sometimes up to 20-30) to hire one.
Assessing candidates before an interview consists of two parts: (a) qualification and (b) “culture fit” assessment.
A conventional method for assessment of qualifications can include having candidates pass some automatic tests, however, in a situation with a lack of candidates, the candidates are not willing to spend a few hours to pass some tests, so this method is often inefficient, as it might turn away good candidates.
At the same time, for many candidates, there is enough publicly available information to assess qualification of a candidate including (a) his/her career progression based on the information in a resume or in a profile on a professional social network; (b) proficiency in a particular technology of companies the candidate worked at; (c) “online reputation” of a candidate consisting of “votes” or “likes”—when peers vote for (or “like” good answers (or good questions) the candidate gives on highly professional forums; or (d) for software developers there might be even available source code they have written for open-source projects and (e) peer-review of the source code—other developers can “fork” code written by this developer or put a star on the code (“like” it) or “follow” the developer. All this information allows assessing qualification of a candidate prior to an interview.
Assessing “culture fit” is a problem that is often missed by someone out of the recruitment industry, but experienced recruiters know that the probability of a particular candidate to be hired by a particular hiring manager is significantly higher if the candidate and the hiring manager have graduated from the same educational institution or worked in the same set of companies in the past, or grown up in the same geographical area, or (in case of software developers) participated in the same open-source project.
Currently it is hard for recruiters to try assessing candidates by both criteria—qualification and “culture fit.” It is just impossible to assess “culture fit” when a recruiter sees a profile with only some information available—e.g., viewing Github profile containing only nickname and email of a candidate.